Single-cell immunology: Past, present, and future

Cell- and tissue-specific glycosylation pathways informed by single-cell transcriptomics

While single-cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ∼40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ∼200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes

Introduction

The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing.

Methods

Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, αβ T cell receptor transcripts and γδ T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs.

Results

Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and γδ T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis identified a subset of CD8+ cytotoxic T cells with skewed TRA and TRB gene usage, conserved TRA and TRB junctional motifs, restricted TRA/TRB pairing and reduced diversity in TRG junctional length. We also identified a public γδ T cell subset with invariant TRD and TRG chains and a CD4+ TEM-like phenotype. Among monocytic cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster.

Discussion

Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

A unique human cord blood CD8+CD45RA+CD27+CD161+ T-cell subset identified by flow cytometric data analysis using Seurat

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

OMIP-102: 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells

We report the development of an optimized 50-color spectral flow cytometry panel designed for the in-depth analysis of the immune system in human blood and tissues, with the goal of maximizing the amount of information that can be collected using currently available flow cytometry platforms. We established and tested this panel using peripheral blood mononuclear cells (PBMCs), but included CD45 to enable its future use for the analysis of human tissue samples. The panel contains lineage markers for all major immune cell subsets, and an extensive set of phenotyping markers focused on the activation and differentiation status of the T cell and dendritic cell (DC) compartment. We outline the biological insight that can be gained from the simultaneous measurement of such a large number of proteins and propose that this approach provides a unique opportunity for the comprehensive exploration of the immune status in human samples with a limited number of cells. Of note, we tested the panel to be compatible with cell sorting for further downstream applications. Furthermore, to facilitate the wide-spread implementation of such a panel across different cohorts and samples, we established a trimmed-down 45-color version which can be used with different spectral cytometry platforms. Finally, to generate this panel, we utilized not only existing panel design guidelines, but also developed new metrics to systematically identify the optimal combination of 50 fluorochromes and evaluate fluorochrome-specific resolution in the context of a 50-color unmixing matrix.

Single cell RNA-sequencing of feline peripheral immune cells with V(D)J repertoire and cross species analysis of T lymphocytes

Introduction

The domestic cat (Felis catus) is a valued companion animal and a model for virally induced cancers and immunodeficiencies. However, species-specific limitations such as a scarcity of immune cell markers constrain our ability to resolve immune cell subsets at sufficient detail. The goal of this study was to characterize circulating feline T cells and other leukocytes based on their transcriptomic landscape and T-cell receptor repertoire using single cell RNA-sequencing.

Methods

Peripheral blood from 4 healthy cats was enriched for T cells by flow cytometry cell sorting using a mouse anti-feline CD5 monoclonal antibody. Libraries for whole transcriptome, alpha/beta T cell receptor transcripts and gamma/delta T cell receptor transcripts were constructed using the 10x Genomics Chromium Next GEM Single Cell 5' reagent kit and the Chromium Single Cell V(D)J Enrichment Kit with custom reverse primers for the feline orthologs.

Results

Unsupervised clustering of whole transcriptome data revealed 7 major cell populations - T cells, neutrophils, monocytic cells, B cells, plasmacytoid dendritic cells, mast cells and platelets. Sub cluster analysis of T cells resolved naive (CD4+ and CD8+), CD4+ effector T cells, CD8+ cytotoxic T cells and gamma/delta T cells. Cross species analysis revealed a high conservation of T cell subsets along an effector gradient with equitable representation of veterinary species (horse, dog, pig) and humans with the cat. Our V(D)J repertoire analysis demonstrated a skewed T-cell receptor alpha gene usage and a restricted T-cell receptor gamma junctional length in CD8+ cytotoxic T cells compared to other alpha/beta T cell subsets. Among myeloid cells, we resolved three clusters of classical monocytes with polarization into pro- and anti-inflammatory phenotypes in addition to a cluster of conventional dendritic cells. Lastly, our neutrophil sub clustering revealed a larger mature neutrophil cluster and a smaller exhausted/activated cluster.

Discussion

Our study is the first to characterize subsets of circulating T cells utilizing an integrative approach of single cell RNA-sequencing, V(D)J repertoire analysis and cross species analysis. In addition, we characterize the transcriptome of several myeloid cell subsets and demonstrate immune cell relatedness across different species.

Dissection and integration of bursty transcriptional dynamics for complex systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

The Impact of Immune System Aging on Infectious Diseases

Immune system aging is becoming a field of increasing public health interest because of prolonged life expectancy, which is not paralleled by an increase in health expectancy. As age progresses, innate and adaptive immune systems undergo changes, which are defined, respectively, as inflammaging and immune senescence. A wealth of available data demonstrates that these two conditions are closely linked, leading to a greater vulnerability of elderly subjects to viral, bacterial, and opportunistic infections as well as lower post-vaccination protection. To face this novel scenario, an in-depth assessment of the immune players involved in this changing epidemiology is demanded regarding the individual and concerted involvement of immune cells and mediators within endogenous and exogenous factors and co-morbidities. This review provides an overall updated description of the changes affecting the aging immune system, which may be of help in understanding the underlying mechanisms associated with the main age-associated infectious diseases.

Single-cell investigative genetics: Single-cell data produces genotype distributions concentrated at the true genotype across all mixture complexities

In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor's contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster's content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes' rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least 1-α. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>1018, showing the potential of single-cell data to positively affect investigative reporting.

Optical diffraction tomography for assessing single cell models in angular light scattering

Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.

Cell and tissue-specific glycosylation pathways informed by single-cell transcriptomics

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ~40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ~200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells

We report the development of an optimized 50-color spectral flow cytometry panel designed for the in-depth analysis of the immune system in human blood and tissues, with the goal of maximizing the amount of information that can be collected using currently available flow cytometry platforms. We established and tested this panel using peripheral blood mononuclear cells (PBMCs), but included CD45 to enable its use for the analysis of human tissue samples. The panel contains lineage markers for all major immune cell subsets, and an extensive set of phenotyping markers focused on the activation and differentiation status of the T cell and dendritic cell (DC) compartment. We outline the biological insight that can be gained from the simultaneous measurement of such a large number of proteins and propose that this approach provides a unique opportunity for the comprehensive exploration of the immune status in tissue biopsies and other human samples with a limited number of cells. Of note, we tested the panel to be compatible with cell sorting for further downstream applications. Furthermore, to facilitate the wide-spread implementation of such a panel across different cohorts and samples, we established a trimmed-down 45-color version which can be used with different spectral cytometry platforms. Finally, to generate this panel, we utilized not only existing panel design guidelines, but also developed new metrics to systematically identify the optimal combination of 50 fluorochromes and evaluate fluorochrome-specific resolution in the context of a 50-color unmixing matrix.

Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary

Cancer mortality primarily stems from metastatic recurrence, emphasizing the urgent need for developing effective metastasis-targeted immunotherapies. To better understand the cellular and molecular events shaping metastatic niches, we used a spontaneous breast cancer lung metastasis model to create a single-cell atlas spanning different metastatic stages and regions. We found that premetastatic lungs are infiltrated by inflammatory neutrophils and monocytes, followed by the accumulation of suppressive macrophages with the emergence of metastases. Spatial profiling revealed that metastasis-associated immune cells were present in the metastasis core, with the exception of TREM2+ regulatory macrophages uniquely enriched at the metastatic invasive margin, consistent across both murine models and human patient samples. These regulatory macrophages (Mreg) contribute to the formation of an immune-suppressive niche, cloaking tumor cells from immune surveillance. Our study provides a compendium of immune cell dynamics across metastatic stages and niches, informing the development of metastasis-targeting immunotherapies.

SIGNIFICANCE

Temporal and spatial single-cell analysis of metastasis stages revealed new players in modulating immune surveillance and suppression. Our study highlights distinct populations of TREM2 macrophages as modulators of the microenvironment in metastasis, and as the key immune determinant defining metastatic niches, pointing to myeloid checkpoints to improve therapeutic strategies. This article is featured in Selected Articles from This Issue, p. 2489.

Guidelines for mouse and human DC functional assays

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Recent studies have provided evidence for an increasing number of phenotypically distinct conventional DC (cDC) subsets that on one hand exhibit a certain functional plasticity, but on the other hand are characterized by their tissue- and context-dependent functional specialization. Here, we describe a selection of assays for the functional characterization of mouse and human cDC. The first two protocols illustrate analysis of cDC endocytosis and metabolism, followed by guidelines for transcriptomic and proteomic characterization of cDC populations. Then, a larger group of assays describes the characterization of cDC migration in vitro, ex vivo, and in vivo. The final guidelines measure cDC inflammasome and antigen (cross)-presentation activity. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis

The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.

Sanguisorba officinalis ethyl acetate extract attenuates ulcerative colitis through inhibiting PI3K-AKT/NF-κB/ STAT3 pathway uncovered by single-cell RNA sequencing

BACKGROUND

Ulcerative colitis (UC) accounts for the untreatable illness nowadays. Bloody stools are the primary symptom of UC, and the first-line drugs used to treat UC are associated with several drawbacks and negative side effects. S. officinalis has long been used as a medicine to treat intestinal infections and bloody stools. However, what the precise molecular mechanism, the exact etiology, and the material basis of the disease remain unclear.

PURPOSE

This work aimed to comprehensively explore pharmacological effects as well as molecular mechanisms underlying the active fraction of S. officinalis, and to produce a comprehensive and brand-new guideline map of its chemical base and mechanism of action.

METHODS

First, different polarity S. officinalis extracts were orally administered to the DSS-induced UC model mice for the sake of investigating its active constituents. Using the UPLC-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap-HRMS) technique, the most active S. officinalis (S. officinalis ethyl acetate fraction, SOEA) extract was characterized. Subsequently, the effectiveness of its active fraction on UC was evaluated through phenotypic observation (such as weight loss, colon length, and stool characteristics), and histological examination of pathological injuries, mRNA and protein expression. Cell profile, cell-cell interactions and molecular mechanisms of SOEA in different cell types of the colon tissue from UC mice were described using single-cell RNA sequencing (scRNA-seq). As a final step, the molecular mechanisms were validated by appropriate molecular biological methods.

RESULTS

For the first time, this study revealed the significant efficacy of SOEA in the treatment of UC. SOEA reduced DAI and body weight loss, recovered the colon length, and mitigated colonic pathological injuries along with mucosal barrier by promoting goblet cell proliferation. Following treatment with SOEA, inflammatory factors showed decreased mRNA and protein expression. SOEA restored the dynamic equilibrium of cell profile and cell-cell interactions in colon tissue. All of these results were attributed to the ability of SOEA to inhibit the PI3K-AKT/NF-κB/STATAT pathway.

CONCLUSIONS

By integrating the chemical information of SOEA derived from UPLC-Q-Orbitrap-HRMS with single-cell transcriptomic data extracted from scRNA-seq, this study demonstrates that SOEA exerts the therapeutic effect through suppressing PI3K-AKT/NF-B/STAT3 pathway to improve clinical symptoms, inflammatory response, mucosal barrier, and intercellular interactions in UC, and effectively eliminates the interference of cellular heterogeneity.

Macrophage heterogeneity in the single-cell era: facts and artifacts

In this spotlight, we review technical issues that compromise single-cell analysis of tissue macrophages, including limited and unrepresentative yields, fragmentation and generation of remnants, and activation during tissue disaggregation. These issues may lead to a misleading definition of subpopulations of macrophages and the expression of macrophage-specific transcripts by unrelated cells. Recognition of the technical limitations of single-cell approaches is required in order to map the full spectrum of tissue-resident macrophage heterogeneity and assess its biological significance.

The early life immune dynamics and cellular drivers at single-cell resolution in lamb forestomachs and abomasum

BACKGROUND

Four-chambered stomach including the forestomachs (rumen, reticulum, and omasum) and abomasum allows ruminants convert plant fiber into high-quality animal products. The early development of this four-chambered stomach is crucial for the health and well-being of young ruminants, especially the immune development. However, the dynamics of immune development are poorly understood.

RESULTS

We investigated the early gene expression patterns across the four-chambered stomach in Hu sheep, at 5, 10, 15, and 25 days of age. We found that forestomachs share similar gene expression patterns, all four stomachs underwent widespread activation of both innate and adaptive immune responses from d 5 to 25, whereas the metabolic function were significantly downregulated with age. We constructed a cell landscape of the four-chambered stomach using single-cell sequencing. Integrating transcriptomic and single-cell transcriptomic analyses revealed that the immune-associated module hub genes were highly expressed in T cells, monocytes and macrophages, as well as the defense-associated module hub genes were highly expressed in endothelial cells in the four-stomach tissues. Moreover, the non-immune cells such as epithelial cells play key roles in immune maturation. Cell communication analysis predicted that in addition to immune cells, non-immune cells recruit immune cells through macrophage migration inhibitory factor signaling in the forestomachs.

CONCLUSIONS

Our results demonstrate that the immune and defense responses of four stomachs are quickly developing with age in lamb's early life. We also identified the gene expression patterns and functional cells associated with immune development. Additionally, we identified some key receptors and signaling involved in immune regulation. These results help to understand the early life immune development at single-cell resolution, which has implications to develop nutritional manipulation and health management strategies based on specific targets including key receptors and signaling pathways.

Spatio-temporal heterogeneity in cancer evolution and tumor microenvironment of renal cell carcinoma with tumor thrombus

Metastasis is the most fatal aspect of cancer, often preceded by a tumor thrombus (TT) which forms within the vascular system. Renal cell carcinoma (RCC), the predominant form of kidney cancer, witnesses a venous system invasion in 4-10% of cases, resulting in venous tumor thrombus (RCC-TT). This variant represents a formidable clinical challenge due to its escalated surgical complexity, heightened risk of progression and metastasis, and an adverse prognosis. However, recent trials addressing RCC-TT face significant barriers stemming from the profound inter- and intra-tumoral heterogeneity, patient-specific treatment variations, and distinct therapeutic resistance patterns between the primary tumor (PT) and the TT. This review delves into the unique evolutionary pathway of RCC-TT, the relationship between the staging and grading of RCC-TT invasion patterns, and the spatial molecular profiling of RCC-TT. Additionally, we assess the temporal heterogeneity among TT, PT, and distant metastases, as well as the functional phenotypes of TME components. An outlook for future research on RCC-TT is also provided.

Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Three-dimensional angular scattering simulations inform analysis of scattering from single cells

Significance

Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens.

Aim

Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates.

Approach

We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles' scattering, and detector noise. We quantified each noise source's effect upon the estimated mean and standard deviation of organelle size distributions.

Results

The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%.

Conclusions

The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.

Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data

Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-seq data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed a novel approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our novel use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

Deciphering the tumour immune microenvironment cell by cell

Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.

Genomic Analysis of Foxp3 Function in Regulatory T Cells

Regulatory T (Treg) cells are critical for tolerance to self-antigens and for preventing autoimmunity. Foxp3 has been identified as a Treg cell lineage-defining transcription factor controlling Treg cell differentiation and function. In this article, we review the current mechanistic and systemic understanding of Foxp3 function enabled by experimental and computational advances in high-throughput genomics.

Dendritic cell subsets in cancer immunity and tumor antigen sensing

Dendritic cells (DCs) exhibit a specialized antigen-presenting function and play crucial roles in both innate and adaptive immune responses. Due to their ability to cross-present tumor cell-associated antigens to naïve T cells, DCs are instrumental in the generation of specific T-cell-mediated antitumor effector responses in the control of tumor growth and tumor cell dissemination. Within an immunosuppressive tumor microenvironment, DC antitumor functions can, however, be severely impaired. In this review, we focus on the mechanisms of DC capture and activation by tumor cell antigens and the role of the tumor microenvironment in shaping DC functions, taking advantage of recent studies showing the phenotype acquisition, transcriptional state and functional programs revealed by scRNA-seq analysis. The therapeutic potential of DC-mediated tumor antigen sensing in priming antitumor immunity is also discussed.

CD4+ T cells produce GM-CSF and drive immune-mediated glomerular disease by licensing monocyte-derived cells to produce MMP12

GM-CSF in glomerulonephritisDespite glomerulonephritis being an immune-mediated disease, the contributions of individual immune cell types are not clear. To address this gap in knowledge, Paust et al. characterized pathological immune cells in samples from patients with glomerulonephritis and in samples from mice with the disease. The authors found that CD4+ T cells producing granulocyte-macrophage colony-stimulating factor (GM-CSF) licensed monocytes to promote disease by producing matrix metalloproteinase 12 and disrupting the glomerular basement membrane. Targeting GM-CSF to inhibit this axis reduced disease severity in mice, implicating this cytokine as a potential therapeutic target for patients with glomerulonephritis. -CM.

Cyclone: an accessible pipeline to analyze, evaluate and optimize multiparametric cytometry data

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

Identification of astrocyte regulators by nucleic acid cytometry

Multiple sclerosis is a chronic inflammatory disease of the central nervous system1. Astrocytes are heterogeneous glial cells that are resident in the central nervous system and participate in the pathogenesis of multiple sclerosis and its model experimental autoimmune encephalomyelitis2,3. However, few unique surface markers are available for the isolation of astrocyte subsets, preventing their analysis and the identification of candidate therapeutic targets; these limitations are further amplified by the rarity of pathogenic astrocytes. Here, to address these challenges, we developed focused interrogation of cells by nucleic acid detection and sequencing (FIND-seq), a high-throughput microfluidic cytometry method that combines encapsulation of cells in droplets, PCR-based detection of target nucleic acids and droplet sorting to enable in-depth transcriptomic analyses of cells of interest at single-cell resolution. We applied FIND-seq to study the regulation of astrocytes characterized by the splicing-driven activation of the transcription factor XBP1, which promotes disease pathology in multiple sclerosis and experimental autoimmune encephalomyelitis4. Using FIND-seq in combination with conditional-knockout mice, in vivo CRISPR-Cas9-driven genetic perturbation studies and bulk and single-cell RNA sequencing analyses of samples from mouse experimental autoimmune encephalomyelitis and humans with multiple sclerosis, we identified a new role for the nuclear receptor NR3C2 and its corepressor NCOR2 in limiting XBP1-driven pathogenic astrocyte responses. In summary, we used FIND-seq to identify a therapeutically targetable mechanism that limits XBP1-driven pathogenic astrocyte responses. FIND-seq enables the investigation of previously inaccessible cells, including rare cell subsets defined by unique gene expression signatures or other nucleic acid markers.

Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma

Glioblastoma (GBM) is the most malignant tumor in center nervous system. Clinical statistics revealed that senior GBM patients had a worse overall survival (OS) comparing with that of patients in other ages, which is mainly related with tumor microenvironment including tumor-associated immune cells in particular. However, the immune heterogeneity and age-related prognosis in GBM are under studied. Here we developed a machine learning-based method to integrate public large-scale single-cell RNA sequencing (scRNA-seq) datasets to establish a comprehensive atlas of immune cells infiltrating in cross-age GBM. We found that the compositions of the immune cells are remarkably different across ages. Brain-resident microglia constitute the majority of glioblastoma-associated macrophages (GAMs) in patients, whereas dramatic elevation of extracranial monocyte-derived macrophages (MDMs) is observed in GAMs of senior patients, which contributes to the worse prognosis of aged patients. Further analysis suggests that the increased MDMs arisen from excessive recruitment and proliferation of peripheral monocytes not only lead to the T cell function inhibition in GBM, but also stimulate tumor cells proliferation via VEGFA secretion. In summary, our work provides new cues for the correlational relationship between the immune microenvironment of GBM and aging, which might be insightful for precise and effective therapeutic interventions for senior GBM patients.

COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas

Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb (https://db.combat.ox.ac.uk). This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.

Single-cell transcriptomics reveals striking heterogeneity and functional organization of dendritic and monocytic cells in the bovine mesenteric lymph node

Dendritic and monocytic cells co-operate to initiate and shape adaptive immune responses in secondary lymphoid tissue. The complexity of this system is poorly understood, also because of the high phenotypic and functional plasticity of monocytic cells. We have sequenced mononuclear phagocytes in mesenteric lymph nodes (LN) of three adult cows at the single-cell level, revealing ten dendritic-cell (DC) clusters and seven monocyte/macrophage clusters with clearly distinct transcriptomic profiles. Among DC, we defined LN-resident subsets and their progenitors, as well as subsets of highly activated migratory DC differing in transcript levels for T-cell attracting chemokines. Our analyses also revealed a potential differentiation path for cDC2, resulting in a cluster of inflammatory cDC2 with close transcriptional similarity to putative DC3 and monocyte-derived DC. Monocytes and macrophages displayed sub-clustering mainly driven by pro- or anti-inflammatory expression signatures, including a small cluster of cycling, presumably self-renewing, macrophages. With this transcriptomic snapshot of LN-derived mononuclear phagocytes, we reveal functional properties and differentiation trajectories in a "command center of immunity", and identify elements that are conserved across species.

Revisiting Aire and tissue-restricted antigens at single-cell resolution

The thymus is a highly specialized organ that plays an indispensable role in the establishment of self-tolerance, a process characterized by the "education" of developing T-cells. To provide competent T-cells tolerant to self-antigens, medullary thymic epithelial cells (mTECs) orchestrate negative selection by ectopically expressing a wide range of genes, including various tissue-restricted antigens (TRAs). Notably, recent advancements in the high-throughput single-cell analysis have revealed remarkable heterogeneity in mTECs, giving us important clues for dissecting the mechanisms underlying TRA expression. We overview how recent single-cell studies have furthered our understanding of mTECs, with a focus on the role of Aire in inducing mTEC heterogeneity to encompass TRAs.

Single‐Cell Profiling Reveals Functional Heterogeneity and Serial Killing in Human Peripheral and Ex Vivo‐Generated CD34+ Progenitor‐Derived Natural Killer Cells

Increasing evidence suggests that natural killer (NK) cells are composed of distinct functional subsets. This multifunctional role has made them an attractive choice for anticancer immunotherapy. A functional NK cell repertoire is generated through cellular education, resulting in a heterogeneous NK cell population with distinct capabilities responding to different stimuli. The application of a high-throughput droplet-based microfluidic platform allows monitoring of NK cell-target cell interactions at the single-cell level and in real-time. A variable response of single NK cells toward different target cells is observed, and a distinct population of NK cells (serial killers) capable of inducing multiple target lysis is identified. By assessing the cytotoxic dynamics, it is shown that single umbilical cord blood-derived CD34+ hematopoietic progenitor (HPC)-NK cells display superior antitumor cytotoxicity. With an integrated analysis of cytotoxicity and cytokine secretion, it is shown that target cell interactions augment cytotoxic as well as secretory behavior of NK cells. By providing an integrated assessment of NK cell functions by microfluidics, this study paves the way to further functionally characterize NK cells ultimately aimed to improve cancer immunotherapy.

Macrophage immunotherapy: overcoming impediments to realize promise

As an essential component of immunity, macrophages have key roles in mammalian host defense, tissue homeostasis, and repair, as well as in disease pathogenesis and pathophysiology. A source of fascination and extensive research, in this Opinion we challenge the utility of the M1-M2 paradigm, and discuss the importance of accurate characterization of human macrophages. We comment on the application of single cell analytics to define macrophage subpopulations and how this could advance therapeutic options. We argue that human macrophage cell therapy can be used to alleviate many diseases, and offer a viewpoint on the knowledge gaps that must be filled to render such a therapeutic approach a reality and, ideally, a common future practice in precision medicine.

Immune checkpoint therapy and response biomarkers in non-small-cell lung cancer: Serum NY-ESO-1 and XAGE1 antibody as predictive and monitoring markers

Immune checkpoint inhibitors (ICI) are key drugs in systemic therapy for advanced non-small-cell lung cancer (NSCLC) and have recently been incorporated into neoadjuvant and adjuvant settings for surgical resection. Currently, ICI combinations with cytotoxic agents are frequently used in clinical practice, although several ICI clinical trials have failed to produce long-term clinical benefits. Unfortunately, clinical benefit is moderate and limited considering physical and financial burden. Therefore, selecting appropriate patients and regimens for ICI therapy is important, and biomarkers are necessary for their selection. Tumor PD-L1 expression is universally used as a biomarker; however, PD-L1 assays show low analytical validity and reproducibility due to the visual-scoring system by pathologists. Recent tumor immunology studies explore that neoantigens derived from somatic mutations and the collaboration between T and B cells efficiently elicit antitumor responses. This suggests that high tumor mutational burden and T-cell infiltration are predictive biomarkers. However, B cells producing antibody (Ab) remain poorly understood and analyzed as biomarkers. We found that NY-ESO-1 and XAGE1 of cancer-testis antigen frequently elicit spontaneous humoral and cellular immune responses in NSCLC. Serum Ab against these antigens were detected in approximately 25% of NSCLC patients and predicted ICI monotherapy responses. In addition, the Ab levels were decreased with tumor shrinkage after ICI therapy. Thus, NY-ESO-1 and XAGE1 Ab are potentially biomarkers predicting and monitoring response to ICI therapy. For clinical applications, a fully-automated assay system measuring the Ab was developed. Here, we review current ICI therapy, tumor immunology, and biomarkers in NSCLC, and discuss the applicability of the serum biomarkers NY-ESO-1 and XAGE1 Ab.

The industrial genomic revolution: A new era in neuroimmunology

Recent discoveries have highlighted the importance of understanding the complex interactions between the brain and immune systems in health and neurodegenerative disease. In this Primer, we outline single-cell multiomics approaches. Applied to patient samples with rich metadata, functional organoids, and animal models, single-cell studies will facilitate the next big leap in translational neuro-immune research. We believe this will pave the way for reshaping our understanding of the neuro-immune interplay: from descriptive to functional, from broad cell types to effective pathways, spatial organization, biomarkers, and targets, toward a comprehensive mechanistic understanding that will be the impetus for drug discovery and therapeutic breakthroughs. We envision that in the near future, single-cell multiomics technologies, along with the advances in immunotherapy development, will become a major driving force and fully integrated resource in the toolset for the development of therapeutic agents in neuroimmunology, which will revolutionize drug development for treating neurodegenerative diseases.

Fibronectin-1 is a dominant mechanism for rheumatoid arthritis via the mediation of synovial fibroblasts activity

Rheumatoid arthritis (RA) has a high incidence and adverse effects on patients, thus posing a serious threat to people’s life and health. However, the underlying mechanisms regarding the development of RA are still elusive. Herein, we aimed to evaluate the RA-associated molecular mechanisms using the scRNA-seq technique. We used the GEO database to obtain scRNA-seq datasets for synovial fibroblasts (SFs) from RA cases, and the genes were then analyzed using principal component analysis (PCA) and T-Stochastic Neighbor Embedding (TSNE) analyses. Bioinformatics evaluations were carried out for asserting the highly enriched signaling pathways linked to the marker genes, and the key genes related to RA initiation were further identified. According to the obtained results, 3 cell types (0, 1, and 2) were identified by TSNE and some marker genes were statistically upregulated in cell type 1 than the other cell types. These marker genes predominantly contributed to extracellular matrix (ECM) architecture, collagen-harboring ECM, and ECM structural components, and identified as enriched with PI3K/AKT signaling cascade. Notably, fibronectin-1 (FN-1) has been identified as a critical gene that is strongly linked to the development of SFs and has enormous promise for regulating the onset of RA. Moreover, such an investigation offers novel perspectives within onset/progression of RA, suggesting that FN-1 may be a key therapeutic target for RA therapies.

HTCA: a database with an in-depth characterization of the single-cell human transcriptome

Single-cell RNA-sequencing (scRNA-seq) is one of the most used single-cell omics in recent decades. The exponential growth of single-cell data has immense potential for large-scale integration and in-depth explorations that are more representative of the study population. Efforts have been made to consolidate published data, yet extensive characterization is still lacking. Many focused on raw-data database constructions while others concentrate mainly on gene expression queries. Hereby, we present HTCA (www.htcatlas.org), an interactive database constructed based on ∼2.3 million high-quality cells from ∼3000 scRNA-seq samples and comprised in-depth phenotype profiles of 19 healthy adult and matching fetal tissues. HTCA provides a one-stop interactive query to gene signatures, transcription factor (TF) activities, TF motifs, receptor-ligand interactions, enriched gene ontology (GO) terms, etc. across cell types in adult and fetal tissues. At the same time, HTCA encompasses single-cell splicing variant profiles of 16 adult and fetal tissues, spatial transcriptomics profiles of 11 adult and fetal tissues, and single-cell ATAC-sequencing (scATAC-seq) profiles of 27 adult and fetal tissues. Besides, HTCA provides online analysis tools to perform major steps in a typical scRNA-seq analysis. Altogether, HTCA allows real-time explorations of multi-omics adult and fetal phenotypic profiles and provides tools for a flexible scRNA-seq analysis.

Three-dimensional genome organization in immune cell fate and function

Immune cell development and activation demand the precise and coordinated control of transcriptional programmes. Three-dimensional (3D) organization of the genome has emerged as an important regulator of chromatin state, transcriptional activity and cell identity by facilitating or impeding long-range genomic interactions among regulatory elements and genes. Chromatin folding thus enables cell type-specific and stimulus-specific transcriptional responses to extracellular signals, which are essential for the control of immune cell fate, for inflammatory responses and for generating a diverse repertoire of antigen receptor specificities. Here, we review recent findings connecting 3D genome organization to the control of immune cell differentiation and function, and discuss how alterations in genome folding may lead to immune dysfunction and malignancy.

WAT3R: Recovery of T Cell Receptor Variable Regions From 3' Single-Cell RNA-Sequencing

SUMMARY

Diversity of the T cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and β chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells (PBMCs) from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T cell types, indicating an association between T cell clonotypes and differentiation states.

AVAILABILITY AND IMPLEMENTATION

The Workflow for Association of T cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r), and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deconstructing iNKT cell development at single-cell resolution

Invariant natural killer T (iNKT) cells are increasingly regarded as disease biomarkers and immunotherapeutic targets. However, a greater understanding of their biology is necessary to effectively target these cells in the clinic. The discovery of iNKT1/2/17 cell effector subsets was a milestone in our understanding of iNKT cell development and function. Recent transcriptomic studies have uncovered an even greater heterogeneity and challenge our understanding of iNKT cell ontogeny and effector differentiation. We propose a refined model whereby iNKT cells differentiate through a dynamic and continuous instructive process that requires the accumulation and integration of various signals within the thymus or peripheral tissues. Within this framework, we question the existence of true iNKT2 cells and discuss the parallels between mouse and human iNKT cells.

Myocardial-Treg Crosstalk: How to Tame a Wolf

The immune system plays a vital role in maintaining tissue integrity and organismal homeostasis. The sudden stress caused by myocardial infarction (MI) poses a significant challenge for the immune system: it must quickly substitute dead myocardial with fibrotic tissue while controlling overt inflammatory responses. In this review, we will discuss the central role of myocardial regulatory T-cells (Tregs) in orchestrating tissue repair processes and controlling local inflammation in the context of MI. We herein compile recent advances enabled by the use of transgenic mouse models with defined cardiac antigen specificity, explore whole-heart imaging techniques, outline clinical studies and summarize deep-phenotyping conducted by independent labs using single-cell transcriptomics and T-cell repertoire analysis. Furthermore, we point to multiple mechanisms and cell types targeted by Tregs in the infarcted heart, ranging from pro-fibrotic responses in mesenchymal cells to local immune modulation in myeloid and lymphoid lineages. We also discuss how both cardiac-specific and polyclonal Tregs participate in MI repair. In addition, we consider intriguing novel evidence on how the myocardial milieu takes control of potentially auto-aggressive local immune reactions by shaping myosin-specific T-cell development towards a regulatory phenotype. Finally, we examine the potential use of Treg manipulating drugs in the clinic after MI.

A topology perspective on macrophages in melanoma metastasis

In this issue of Cell Reports Medicine, Martinek et al.¹ provide a window into the regional specialization of macrophages infiltrating metastatic melanoma. Combining histo-cytometry and transcriptomics, they identify a signature of stromal macrophages with dendritic cell features and clinical relevance.

Human dendritic cells in cancer

Dendritic cells (DCs) are professional antigen-presenting cells, orchestrating innate and adaptive immunity during infections, autoimmune diseases, and malignancies. Since the discovery of DCs almost 50 years ago, our understanding of their biology in humans has increased substantially. Here, we review both antitumor and tolerogenic DC responses in cancer and discuss lineage-specific contributions by their functionally specialized subsets, including the conventional DC (cDC) subsets cDC1 and cDC2, the newly described DC3, and the plasmacytoid DCs (pDCs), focusing on the human setting. In addition, we review the lineage-unrestricted "mature DCs enriched in immunoregulatory molecules" (mregDC) state recently described across different human tumors.